Robotic arms are used in laboratory automation, for instance in the processing of tubes such as sample tubes as used in healthcare diagnostics. Traditionally, the robotic arm is equipped with a bar-code reader. In order to operate tube samples, the robotic arm will try to grab a tube at a given location and it uses a mechanical sensor to determine whether there is a tube or not, which is basically “seeing by touching”.
This “seeing by touching” operation requires a mechanical step which extends the total time for locating a tube in an inventory of tubes. Deciding the presence of a tube at a certain location in an inventory of tubes can take place much faster and with higher accuracy when only images are used and the robotic arm does not have to move first to an assumed tube to determine the existence of a tube in a location. The use of multiple images in a multi-view machine vision approach is believed to significantly improve a performance of a robotic arm in processing sample tubes in healthcare diagnostic. Such an approach is believed not to be currently available.
Accordingly, novel and improved multi-view stereo systems and methods for tube inventory in healthcare diagnostics are required.